Large-scale cosmological simulations are an indispensable tool for modern cosmology. To enable model-space exploration, fast and accurate predictions are critical. In this paper, we show that the performance of such simulations can be further improved with time-stepping schemes that use input from cosmological perturbation theory. Specifically, we introduce a class of time-stepping schemes derived by matching the particle trajectories in a single leapfrog/Verlet drift-kick-drift step to those predicted by Lagrangian perturbation theory (LPT). As a corollary, these schemes exactly yield the analytic Zel'dovich solution in 1D in the pre-shell-crossing regime (i.e. before particle trajectories cross). One representative of this class is the popular FastPM scheme by Feng et al. 2016, which we take as our baseline. We then construct more powerful LPT-inspired integrators and show that they outperform FastPM and standard integrators in fast simulations in two and three dimensions with $\mathcal{O}(1 - 100)$ timesteps, requiring less steps to accurately reproduce the power spectrum and bispectrum of the density field. Furthermore, we demonstrate analytically and numerically that, for any integrator, convergence is limited in the post-shell-crossing regime (to order 3/2 for planar wave collapse), owing to the lacking regularity of the acceleration field, which makes the use of high-order integrators in this regime futile. Also, we study the impact of the timestep spacing and of a decaying mode present in the initial conditions. Importantly, we find that symplecticity of the integrator plays a minor role for fast approximate simulations with a small number of timesteps.
CMB line-of-sight integrators for nearly-isotropic cosmological models
Universidade Estadual de Londrina（朗德里纳州立大学）；Universidade Federal do Rio de Janeiro（里约热内卢联邦大学）；Institut d’Astrophysique de Paris, CNRS UMR 7095（巴黎天体物理研究所）
C3NN-SBI: Learning Hierarchies of $N$-Point Statistics from Cosmological Fields with Physics-Informed Neural Networks
Ludwig-Maximilians-Universität（慕尼黑大学）；Flatiron Institute（平顿研究所）；Excellence Cluster ORIGINS（ORIGINS卓越集群）；Max-Planck-Institut für extraterrestrische Physik（马克斯·普朗克地外物理研究所）；University of Arizona（亚利桑那大学）；Universität Bielefeld（比勒费尔德大学）
Cosmo-PINN: A Physics-Informed Neural Network for Cosmological Reconstruction
Durban University of Technology（德班理工大学）；North-West University（西北大学）；Universidad Catòlica del Norte（北天主教学会）；National Institute for Theoretical and Computational Sciences (NITheCS)（国家理论与计算科学研究所）
Instituto de Física Teórica UAM-CSIC（UAM-CSIC理论物理研究所）；Universidad Autónoma de Madrid（马德里自治大学）；University of Oxford（牛津大学）；Kavli IPMU (WPI), UTIAS, The University of Tokyo（东京大学宇宙研究机构与物理数学国际研究中心卡弗里宇宙起源与演化前沿中心）；Center for Data-Driven Discovery, Kavli IPMU (WPI), UTIAS, The University of Tokyo（东京大学宇宙研究机构与物理数学国际研究中心卡弗里宇宙起源与演化前沿中心数据驱动发现中心）
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter
University of Houston（休斯顿大学）；University of Edinburgh（爱丁堡大学）；Carnegie Mellon University（卡内基梅隆大学）；Pittsburgh Supercomputing Center（匹兹堡超级计算中心）；University of the Western Cape（西开普大学）；South African Astronomical Observatories（南非天文台）
INAF-Osservatorio Astronomico di Trieste（意大利国家天体物理研究所的里雅斯特天文台）；Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians-Universität München（慕尼黑大学物理学院大学天文台）；Max-Planck-Institut für Astrophysik（马克斯·普朗克天体物理学研究所）；Department of Astronomy and Astrophysics, The University of Chicago（芝加哥大学天体物理学系）；IFPU - Institute for Fundamental Physics of the Universe（宇宙基础物理研究所）；ICSC - Italian Research Center on High Performance Computing, Big Data and Quantum Computing（意大利高性能计算、大数据和量子计算研究中心）；INFN, Sezione di Trieste（意大利国家核物理研究所的里雅斯特分部）；Intel Technology India Private Ltd（英特尔技术印度私人有限公司）；Dipartimento di Fisica e Astronomia "Augusto Righi", Alma Mater Studiorum Università di Bologna（博洛尼亚大学奥古斯托·里吉物理与天文系）
Towards end-to-end Bayesian forward models in global 21-cm cosmology: surrogate modelling and marginalisation of beam uncertainty
Astrophysics Group, Cavendish Laboratory（卡文迪许实验室天体物理组）；Kavli Institute for Cosmology（卡弗里宇宙学研究所）；Antenna Group, Université Catholique de Louvain（天主教鲁汶大学天线研究组）
Uniform Rolling: An LSST Observing Cadence Offering Sufficient Survey Uniformity for Comprehensive Cosmological Analysis
Imperial College London（帝国理工学院）；Argonne National Laboratory（阿贡国家实验室）；Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（斯坦福直线加速器中心）；Rutgers, the State University of New Jersey（新泽西州立罗格斯大学）；University College London（伦敦大学学院）；University of Toronto（多伦多大学）；Duke University（杜克大学）；University of the Western Cape（西开普敦大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）；University of Cambridge（剑桥大学）；Stockholm University（斯德哥尔摩大学）；University of Washington（华盛顿大学）
